Shadowing Practice: New Skills! v1.3 brings /pr, /implement-spec, and /retro - Learn English Speaking with Video
Creating lesson...
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Hello, folks.
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Version 1 .3 of my skills repo is out, and it brings three very important skills that have become central to my workflow.
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Today, I'm going to show you about implement spec.
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I'm going to show you about PR, and I'm going to show you about retro.
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And retro, I think, is the one that I've just seen absolutely go nuts on socials, people going crazy for this skill,
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and it has felt incredibly powerful to me, too.
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The first place I want to start is the implement spec skill.
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The The main flow of my approach for landing huge chunks of work at once is essentially two different entities.
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You have a spec and then you have the tickets that implement that spec.
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The spec kind of acts like the place where you're going
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and then the tickets break that destination down into a set of sessions
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that you can then run in a single coding agent.
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If you try to implement the spec all in one coding agent, you would go into the dumb zone of that coding agent you might end up hitting autocompact.
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And, you know, while that works sometimes and it is getting better, I still think that breaking things down into individual tickets is just a lot cleaner.
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Now, the way that you orchestrate those tickets, the way that you give them each to one coding agent has always been a little bit confusing for users.
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And I've kind of been sort of going back and forth over the way that I recommend you do this.
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The most annoying and manual way to do this is managing it yourself, where you, the user, say, OK, agent, we are going to implement our first ticket.
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You wait for it to be complete.
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You clear the context.
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And then you say, OK, implement ticket two.
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You wait.
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You clear the context.
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You say implement ticket three.
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You're kind of acting like a for loop there.
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It's not really workable.
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So that is what I would describe as the manual loop there, which is not much fun.
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But you can change that by having a deterministic loop where you have a script
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that reads each ticket and then runs implement on it itself.
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This is how I recommend that most people do this because once you've set up your deterministic loop, it's going to run the same way every time.
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It's very reliable and it's cheap, right?
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Because you're not using an agent or you're not using a human to run that loop.
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You just essentially get a script to do it for you.
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But setting this up is quite complicated
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and requires a lot of patience as you sort of refine your loop and get it better and better.
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And so I see this is out of range for beginners.
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And so I wanted some way of allowing beginners to see not have to manually loop it themselves.
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And so I decided, OK, why don't we try a middle ground where we have subagents go through and implement the loop?
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In other words, an agent does the babysitting instead of a human.
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This means that all of the tickets will be implemented by subagents.
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And this has recently gotten possible because subagents can themselves spawn subagents.
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So before subagents were kind of a little bit nerfed, whereas now they're actually as powerful as orchestrator agents.
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And so this is what implement spec does, is the agent sits there looking at all of the tickets, orchestrating them, and then implements them in subagents.
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This is, I would say, worse than doing a deterministic loop, because the deterministic loop, again, it's deterministic.
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It works the same way every time.
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But implement spec is a good way to get started with AFK workflows, away from keyboard workflows where you step away and it implements the entire spec.
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Before you use any skill, you should read the entire skill.
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And it's not particularly long here.
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There are nine steps in its process, and it's got a little bit of reference material just at the top.
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One crucial thing is it goes for parallelization where it can.
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So this says the tickets are not a list of steps.
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They're a task graph with blocking relationships between them.
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That means there's always a frontier of tickets that are ready to be grabbed.
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If we take a look at the steps, we can see it reads the spec and tickets.
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It does a little bit of exploration in its own subagent.
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It creates an integration branch, and then it goes and uses implementer subagents to implement each ticket using TDD and using work trees too.
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Once that's done, it merges its work to the integration branch with a subagent
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and then pings off more implementer subagents until all the work is done.
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And once the work is done, it goes and calls code review on the integration branch.
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Then it cleans up all of its work and makes the PR ready for review.
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So you get a single PR off this huge great big spec.
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I've been reaching for this a lot more than I thought I would, especially on systems where you haven't set up these deterministic scripts yet,
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where you don't have your software factory dialed in, I suppose, implement spec has been really, really useful.
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It's been in progress for a while now, but I thought now's the moment.
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Let's graduate this so people can use it and fiddle around with it and build their own version of this too.
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This is a basic version of how you might implement your own spec.
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You could do something totally different to this.
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You could use stacked PRs.
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You could do all sorts of stuff.
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And in fact, talking of PRs, one of my most used recent skills has to be the PR skill.
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So prskill .md.
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I've been a huge fan of Dex Hawthies for a while now.
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I recently got to meet up with him in person and he has this gorgeous skill called show me.
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What show me does is it helps the user understand the current topic visually with concise diagrams
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and it uses all of this like really nice pseudocode.
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This looks a little bit garbage but actually
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when you see it in the context of a PR it really makes the PR a lot easier to read and review.
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PRs are still the main bottleneck for work getting to Maine.
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And my goal with this PR skill, kind of inspired by Show Me, is to make human review as easy as possible.
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And in the skill, we provide essentially a template for writing a PR body.
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That's all the skill does.
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We first start with a summary based on the kind of Show Me template, which we include in the skill.
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And then we ask for evidence.
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So before and after.
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This evidence is an essential part of learning to trust your agent, learning to trust your agent's outputs.
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And actually prompting it to provide evidence often means it runs an extra test
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or it takes an extra screenshot if it has that ability.
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And it provides you runtime information on that its actual change is doing what it thinks it's doing.
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Without asking for hard evidence, it's very easy for agents to say, yeah, that probably works because I've read the code.
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But no, verification is something I'm beginning to be really obsessed with, especially after my recent interview with Lauren.
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And then the third heading here is merge danger.
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So how dangerous is this PR going to be to merge?
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One metaphor I really like here is, is this a one -way door or is this a two -way door?
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A two -way door is a door that you can walk back through.
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In other words, we can easily revert this PR.
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But a one -way door is a PR that's going to
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do something out in the world it's going to delete some data
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or it's going to be expensive in some way to roll back
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and then finally you have blast radius here
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so the potential ramifications of the changes so
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if you have a very small blast radius on a two
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-way door then you know you really don't need to like review
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that pr very hard and so having this at the bottom probably the you know first
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or second thing the person is going to see is essential
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so for instance here's one of the prs in my repo that has been provided by the PR scale.
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This is essentially merging two algorithms that were basically the same.
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And we can see the files deleted and the files added in the right context.
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We understand the functions, the sort of the call stack that's being structured here.
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But crucially, we have the evidence.
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We have a before and after on a real entity.
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So there's no intro in this one, but afterwards it adds the intro, which is kind of the behavior that we want here.
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And finally, we can see that the merge danger is two ways, just a pure refactor plus one deliberate behavior change and the blast radius is small.
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So this PR skill has just become absolutely indispensable to my flow.
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It is also one of the most consistently invoked model invoke skills that I've seen.
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You, the user, don't have to worry about it at all.
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It seems to just invoke it every single time, at least on Opus 5 .5.
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So you should just be able to download it and just see your PR bodies get better.
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And of course, if you already have a PR body skill, then why not just steal what you can from this.
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Now, before we talk about the retro skill, we should mention that there has been a big change in how my especially grill with docs function.
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So let's take a look at domain modeling here and domain modeling skill.
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Now, instead of writing to context .md, writes to glossary .md.
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The inspiration for this skill came from DDD, came from Eric Evans's book Domain Driven design.
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And for that, I was essentially using a file name based on the concept of bounded context from DDD.
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And so this used to be context .md like this.
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But now I've sort of moved away from the DDD style flow.
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I mean, I still use DDD in all but name, but I wanted a slightly less overloaded term here.
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Context .md just felt way too vague.
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It didn't sort of trigger the agent to pull it in at the right moment.
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And for users, it was pretty confusing.
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I also ended up reducing the amount of stuff in glossary or in context .md until it was literally just a glossary.
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So there really is no point anymore.
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Glossary .md is now what we're going.
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This is also updated everywhere.
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And so loads of my skills rely on seeing a glossary .md in order to behave properly, in order to use the domain language at all points.
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And so you will need to definitely update this.
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There's no other behavior changes about the skill.
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It's really literally just a file name change.
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And I think that this is one that folks have been wanting for a while.
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So I'm glad to ship it.
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But now let's take a look at the retro skill.
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And let's actually do it by running it.
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What I noticed with my skill set is that I impose a lot.
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I ask a lot of you, the user.
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And what I was kind of asking of you is you were responsible for,
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over time, making changes to the repo that would improve it.
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In other words, I was asking you to manage your agents .md, to manage your skills, to make sure that your repo was token efficient,
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to make sure that your environment was good and safe for the agent to write code in, that you had good lint rules, that you had good navigation.
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And I realized
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that I personally was doing a lot of stuff to my
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repos to my agents in order to make them easier for me to work with, to make them more performant.
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And so I realized, why don't I bundle that up into a skill?
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And this is what retro does.
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It is short for retrospective.
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You run it on a coding agent session, either the current one or 10 that I've currently got, or let's say the previous one,
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and it will suggest improvements for next time.
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The cool thing about actually reading real sessions here is that it's able to look at what happened with the agent.
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It's able to see the inefficiencies that maybe the agent actually hides from.
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I really like this post from Andre.
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Retro by Matt Parker's Game Changer.
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My agents are bumping into all kinds of problems that went undetected, but due to persistence, the features still got built one way or another.
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In other words, the agent doesn't complain, I think, as much as it should and doesn't try to fix its own mistakes.
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You kind of need to get another agent to look back on those sessions
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and see where it went wrong and see how to help.
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And so this is what our agent has found here.
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The agent ran an irreversible public action before you picked a fix.
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The agent found why the 1 .3 .0 release was missing and then it ran a release without actually asking.
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This is why, by the way, when you actually look at the releases, you'll see there's no 1 .3 .0 release, only a 1 .3 .1 release.
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You know, Claude kind of screwed this up.
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I don't honestly care that much about it, so I don't mind this one in my context.
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We can see that this particular repo in has no actual guardrail.
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So there is a PNPM check script, but nothing actually runs it.
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So it's suggesting adding a CI pipeline here.
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And here we can see there's context loss happening between compactions in long sessions.
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So I use animatics to kind of create my videos or early drafts of them now.
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And it's saying that there's some loss when you compact.
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And so it's saying move some of the repeated instructions into an animatic skill.
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This is definitely a really good one.
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And I'm definitely going to implement that for sure.
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Or look at this one.
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A custom CLI that I've created for my course video manager, wastes tokens.
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So Retro looks at token inefficiency too.
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Wiki, which is another custom CLI, is not on path locally and Wiki AI Hero has gaps.
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So Retro is merciless.
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It will look at your actual sessions and see what is going on
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and it will pretty much always find ways to improve.
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Now, what I'd say with Retro is you want to use your human judgment when you look at these.
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I know, for instance, the thing that it thinks is most serious is actually not that bad.
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It's just a release.
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I can always create and cut new releases.
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And so the way retro works is it's designed to be human in the loop.
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It's not designed to just keep creating these fixes random.
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Pretty much everyone when I show them this, they go, oh, I'd love to automate this.
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No, you don't want to automate this.
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Automating this means that the agent will get itself into a loop where it continually finds false positives.
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It continually tries to fix things, and you'll end up in this weird trajectory where it just takes the repo
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and your agent into a place it shouldn't really go.
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I recommend you run retro on a sampling of your agent sessions.
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Every time you've got a free moment and you realize, oh, I haven't run retro in a while, just try getting it to run through your most recent sessions.
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Maybe a session that has gone wrong, let's say, or you feel the agent has done something weird, and retro will usually find a fix.
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We can see that inside the steps in the retro skill, it has several categories for improvements that it looks for.
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How easy is your code base to navigate?
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Are there automated checks that we could add?
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Could we add coding standards for the automated reviewer to enforce?
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How healthy is the global agents .md?
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Is the tool economy any good?
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Are there any no -ops in the instructions and steering files?
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Does the agent have access to all the information it needs?
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Overall, I've seen my token efficiency and just the quality of work
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that I've been able to produce massively increase by running retro regularly.
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And if you want to see the quality of your work increase too, then I recommend you take my AI coding crash course, which is on aihero .dev.
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I'm updating this today with a new lesson focusing on implement spec.
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And if you want to take control of your coding agent and utilize my skills better, then this is the place.
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But folks, thank you so much for following along.
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Hopefully you enjoy skills v1 .3.
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Enjoy seeing the uncomfortable truth of how bad your agent sessions are with Retro, and I'll see you in the next one.
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Speaking Goals for This Clip
This video is perfect for practicing conversational fluency in technical contexts, targeting the ability to explain workflows, tools, and problem-solving steps clearly. You’ll learn to structure explanations with logical flow—ideal for discussing projects, tools, or processes in professional or academic settings. It also helps build confidence in describing both manual and automated systems, a key milestone for advanced English learners.
Phrase Bank
- "brings three very important skills that have become central to my workflow"
- "the main flow of my approach for landing huge chunks of work at once"
- "acts like the place where you're going and then the tickets break that destination down"
- "end up hitting autocompact"
- "orchestrate those tickets"
- "a deterministic loop where you have a script that reads each ticket"
- "out of range for beginners"
- "a middle ground where we have subagents go through and implement the loop"
- "goes for parallelization where it can"
Fix Your Weak Spots
This video is great for refining rhythm and stress in longer sentences, especially those with technical terms. Practice shadow speech by repeating phrases like "the deterministic loop, again, it's deterministic" to master pausing and emphasizing key terms. Focus on reducing hesitation when explaining complex ideas—imitate the speaker’s steady pace. Also, work on pronouncing multi-syllabic words like "orchestrate," "deterministic," and "parallelization" clearly. Use shadow speak techniques: play a short clip, pause, and repeat it immediately, matching the tone and speed. This will help you sound more natural when discussing technical topics in English. For effective english speaking practice, break the video into 30-second segments and shadow each one until you can replicate the flow effortlessly. Learning English with videos like this—full of practical, real-world language—makes improving your speaking skills both engaging and effective.
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Present perfect continuous have/has been + -ing — an action that started earlier and is still going on | I've been reaching · have been wanting |
| Present perfect have/has + past participle — a past action that still matters now | have become · I've just seen · has felt |
| Passive voice be + past participle — the focus is on what happens, not who does it | will be implemented · be grabbed · is done |
| Relative clauses who / which + clause — extra information about a person or thing | there, which is · intro, which is · Wiki, which is |
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.